On the Re-Solving Heuristic for (Binary) Contextual Bandits with Knapsacks

In the problem of (binary) contextual bandits with knapsacks (CBwK), the agent receives an i.i.d. context in each of the $T$ rounds and chooses an action, resulting in a random reward and a random consumption of resources that are related to an i.i.d. external factor. The agent's goal is to maximize the accumulated reward under the initial resource constraints. In this work, we combine the re-solving heuristic, which proved successful in revenue management, with distribution estimation techniques to solve this problem. We consider two different information feedback models, with full and partial information, which vary in the difficulty of getting a sample of the external factor. Under both information feedback settings, we achieve two-way results: (1) For general problems, we show that our algorithm gets an $\widetilde O(T^{\alpha_u} + T^{\alpha_v} + T^{1/2})$ regret against the fluid benchmark. Here, $\alpha_u$ and $\alpha_v$ reflect the complexity of the context and external factor distributions, respectively. This result is comparable to existing results. (2) When the fluid problem is linear programming with a unique and non-degenerate optimal solution, our algorithm leads to an $\widetilde O(1)$ regret. To the best of our knowledge, this is the first $\widetilde O(1)$ regret result in the CBwK problem regardless of information feedback models. We further use numerical experiments to verify our results.Comment: 43 pages, 2 figures, 1 tabl

Multi-Modal Gaze Following in Conversational Scenarios

Gaze following estimates gaze targets of in-scene person by understanding human behavior and scene information. Existing methods usually analyze scene images for gaze following. However, compared with visual images, audio also provides crucial cues for determining human behavior.This suggests that we can further improve gaze following considering audio cues. In this paper, we explore gaze following tasks in conversational scenarios. We propose a novel multi-modal gaze following framework based on our observation ``audiences tend to focus on the speaker''. We first leverage the correlation between audio and lips, and classify speakers and listeners in a scene. We then use the identity information to enhance scene images and propose a gaze candidate estimation network. The network estimates gaze candidates from enhanced scene images and we use MLP to match subjects with candidates as classification tasks. Existing gaze following datasets focus on visual images while ignore audios.To evaluate our method, we collect a conversational dataset, VideoGazeSpeech (VGS), which is the first gaze following dataset including images and audio. Our method significantly outperforms existing methods in VGS datasets. The visualization result also prove the advantage of audio cues in gaze following tasks. Our work will inspire more researches in multi-modal gaze following estimation

Hybrid Si-GaAs photonic crystal cavity for lasing and bistability

The heterogeneous integration of silicon with III-V materials provides a way to overcome silicon's limited optical properties toward a broad range of photonic applications. Hybrid modes are a promising way to make heterogeneous Si/III-V devices, but it is still unclear how to engineer these modes to make photonic crystal cavities. Herein, using 3D finite-difference time-domain simulation, a hybrid Si-GaAs photonic crystal cavity design enables cavity mode confinement in GaAs without directly patterning that operates at telecom wavelengths. The hybrid cavity consists of a patterned silicon waveguide nanobeam that is evanescently coupled to a GaAs slab with quantum dots. We show that by engineering the hybrid modes, we can control the degree of coupling to the active material, which leads to a tradeoff between cavity quality factor and optical gain and nonlinearity. With this design, we demonstrate a cavity mode in the Si-GaAs heterogeneous region, which enables strong interaction with the quantum dots in the GaAs slab for applications such as low-power-threshold lasing and optical bistability (156 nW and 18.1 ${\mu}$W, respectively). This heterogeneous integration of an active III-V material with silicon via a hybrid cavity design suggests a promising approach for achieving on-chip light generation and low-power nonlinear platforms

Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without extensive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs

Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without exten-sive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs

Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without extensive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs

Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation

Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without exten-sive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs

Cavity enhanced emission from a silicon T center

Silicon T centers present the promising possibility to generate optically active spin qubits in an all-silicon device. However, these color centers exhibit long excited state lifetimes and a low Debye-Waller factor, making them dim emitters with low efficiency into the zero-phonon line. Nanophotonic cavities can solve this problem by enhancing radiative emission into the zero-phonon line through the Purcell effect. In this work we demonstrate cavity-enhanced emission from a single T center in a nanophotonic cavity. We achieve a two-orders of magnitude increase in brightness of the zero-phonon line relative to waveguide-coupled emitters, a 23% collection efficiency from emitter to fiber, and an overall emission efficiency into the zero-phonon line of 63.4%. We also observe a lifetime enhancement of 5, corresponding to a Purcell factor exceeding 18 when correcting for the emission to the phonon sideband. These results pave the way towards efficient spin-photon interfaces in silicon photonics.Comment: References update